Educating the Public on Evidence-based methods for improving inter-group civility.

“In-Group Love” without “Out-Group Hate”

“In-Group Love” without “Out-Group Hate”

        Two types of economic games were introduced: the Intergroup Prisoner’s Dilemma game (IPD) and the Intergroup Prisoner’s Dilemma-Maximizing Difference game (IPD-MD). In the IPD game participants allocated tokens between a pool for themselves and a between-group pool (pool B). In the IPD-MD game participants allocated tokens among a pool for themselves, pool B and a within-group pool (pool W). As shown in the pay-off matrices below, a token allocated in pool B will benefit everyone in the group and harm the other group while a token allocated in pool W will benefit everyone in the group without harming the other group. The optimal strategy for individual would be keeping all tokens for themselves. In the IPD game, the optimal strategy for the group would be putting all tokens in pool B. However, if the other group do the same, both groups will gain nothing. In the IPD-MD game contributing to pool W makes a group gain without intergroup competition, but there is no guarantee the other group would do the same.

IPD-MD Payoff Matrix

IPD payoff matrix

1. What They Did – Intervention Summary:

       Participants were randomly assigned to two conditions. In the IPD-MD condition participants played IPD-MD game for 60 rounds. In the IPD condition participants played IPD game for 30 rounds and then IPD-MD game for another 30 rounds. All decisions were made in private using a computer. Participants played in a group of 3 against another group of 3.  Group composition and group matching were kept constant throughout the study. At the end of the study participants were paid for every points they earned.

 2. What They Found – Results:

       For the IPD-MD condition, as can be seen in the picture below, participants contributed on average 31.54% of their endowment to pool W, as compared with only 5.25% to pool B. The rest of the endowment (63.20%) was kept for private use. For the IPD condition, in the first (IPD) part of the interaction, the rate of contribution to pool B was 26.50%. In the second (IPD-MD) part, despite the competition in the first part, the contribution rate to pool B dropped to 5.72%. The present experiment established that out-group hate does not evolve spontaneously in interaction between randomly composed groups, not even after a period of intergroup conflict.

IPD

 3. Who Was Studied – Sample:

Undergraduate Students

 4. Study Name:

Halevy et al., 2012

 5. Citation:

Halevy, N., Weisel, O., & Bornstein, G. (2012). “In‐Group Love” and “Out‐Group Hate” in Repeated Interaction Between Groups. Journal of Behavioral Decision Making, 25(2), 188-195.

 6. Link:

http://www.econ.mpg.de/files/2012/staff/Weisel_JBDM_2011.pdf

 7. Intervention categories:

An opportunity to show ingroup love without outgroup hate

 8. Sample size:

144

 9. Central Reported Statistic:

In the IPD condition, a repeated measure ANOVA with block as a within-subject variable and contribution to pool B as the dependent variable found a highly significant block effect (F(3,33)=25.80, p<.001).

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Effect of Explanation on Understanding and Position Extremity

1. What They Did-Summary:

This study primarily focused on the effects that explaining one’s position on policy can have on feelings of understanding and position extremity.  198 participants were split into two groups.  One group was asked to rate their support or opposition to six different political policies such as instituting a flat tax or instituting a single-payer health care system.  Next, the participants were asked to rate their understanding of these policies.  Afterwards, they were asked to provide a mechanistic explanation for how two of these policies (chosen at random) work.  Lastly, the first group of participants were asked again to rate their position on and understanding of the issues.  The second group differed from the first in that they were only asked to rate their position on and understanding of the issues after they had provided an explanation of the policies.

The researchers predicted that people who hold extreme positions are under the illusion that they know more about policies than they really do.  Thereby, by having the participants explain how these policies work, they should realize this illusion of understanding and shift to a more moderate viewpoint.

2. What They Found-Results:

The researchers found a significant decrease in ratings of understanding following explanations.  They also found that people’s positions on the issues became significantly more moderate following explanations.  These findings confirmed the researchers’ predictions.

3. Who Was Studied-Sample:

198 U.S. residents recruited using MTurk, 52% male, 48% female.  40% Democrat, 20% Republican, 36% independent, 4% other.

4. Study Name:

Fernbach et al. 2013, Study 1.

5. Citation:

Fernbach, P.,  Rogers, T., Fox, C., and Sloman, S. “Political Extremism Is Supported by an Illusion of Understanding.” Psychological Science (2013): 1-8.

6. Link:

http://pss.sagepub.com/content/early/2013/04/24/0956797612464058

7. Intervention Categories:

mechanistic explanation, MTurk, judgment timing

8. Sample Size:

198

9. Central Reported Statistic:

Understanding: “This prediction was confirmed by a significant main effect of judgment timing: Postexplanation ratings of understanding (M = 3.45, SE = 0.12) were lower than preexplanation ratings (M = 3.82, SE = 0.11), F(1, 197) = 34.69, p < .001, ηp2 = .15.”

Position Extremity: “This prediction was confirmed, with the main effect of judgment timing significant (preexplanation-rating conditions: M = 1.41, SE = 0.07; postexplanation-rating conditions: M = 1.28, SE = 0.08), F(1, 86) = 6.10, p = .016, ηp2 = .066.”

10. Effect Size:

Understanding: t(5) = 5.74, p < .01.

Position Extremity: t(5) = 3.93, p = .011.

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Putting Interventions to the Test: A Comparison of Five Techniques to Reduce Partisan Hostility

The growing hostility between liberals and conservatives in the United States is a known problem to many.  However, what to do about it is much less clear.  Various groups, such as the Asteroids Club and the Village Square, have developed their own techniques for promoting civility between the opposing parties.    What my collaborators Matt Motyl, Brian Nosek, Jon Haidt, and I wanted to know was: which strategy is the most effective at reducing partisan hostility?  The following describes the result of our attempt to throw the proverbial “kitchen sink” at this problem, testing the effectiveness of several techniques in one study.  The five interventions we tested come from a collection of active civility groups, past social psychological research, and our own intuitions.

Liberals and conservatives completed our study online, being exposed to one (or none) of our five interventions before completing measures of political attitudes and hostility.  The interventions consisted of:

Self Affirmation- Past social psychological work has demonstrated that being reassured of one’s valued traits leads to less defensive and biased processing of opposing viewpoints. Participants in this condition spent a few minutes writing about a valued personal characteristic and a time that they embodied that trait.

Learning Political Membership Last- People readily form impressions of others, and can be motivated to maintain their opinions in order to remain consistent in their evaluations.  This intervention attempted to leverage this motivation by having participants read about a very positive group of individuals, only to later learn that they had volunteered for the opposing political party.

Observing Civility- People often learn by observing the behaviors of others.  For this intervention, participants watched a video describing the relationship between Republican Ronald Reagan and Democrat Tip O’Neill.  The video described the two as having a very friendly and respectful relationship, even when the two did not see eye to eye.

Superordinate Threat- Having a common threat can bring groups together.  To create this common threat, we had participants read an article describing the threat of cyber warfare attacks on the United States.  The article concluded by stating that bipartisan efforts had the potential to eliminate this threat.

Reducing Zero Sum Perceptions- Much of current political gridlock stems from a perception of legislation as a zero sum game (any win for the other side is automatically a loss for my side).  This final intervention sought to weaken this perception by describing the consequences of this mentality and the ways it is inhibiting progress.  The article concluded by stating that shedding this mindset in favor of increased compromise could help both sides achieve their goals.

After the intervention phase, participants completed a measure of partisan hostility, indicated their explicit liking of Republicans and Democrats, and completed an implicit measure of political attitudes (the Implicit Association Test), which measured the participants’ nonconscious attitudes toward the two groups.  The goal of these interventions was to reduce hostility, not necessarily make participants like the other side more.  As such, we were most interested in seeing whether each of the interventions reduced hostility relative to the group that received no intervention (Control).  The results are displayed below:

Screen Shot 2014-09-22 at 10.44.21 AM

Each dot represents the average hostility score for participants in a given condition (with the red bars marking a 95% confidence interval around that value).  Higher hostility scores are indicative of greater hostility.  These results show that each intervention produced the desired effect, that being lower hostility, but the degree to which they were effective varied.  Reducing Zero Sum Perceptions was the most effective intervention at reducing hostility, closely followed by Superordinate Threat (although Reducing Zero Sum Perceptions was the only intervention to approach statistical significance, p = .052).  Of note, none of the interventions reduced implicit or explicit liking for one’s own party relative to the other party.  In fact, most interventions increased partisan preferences relative to the control condition.  This demonstrates that promoting civility need not reduce an individual’s liking for his or her own group.  Rather, hostility can be specifically targeted and reduced without changing these attitudes.

The results of our intervention contest suggest that there are multiple paths to reducing partisan hostility.  However, not all strategies are equally effective.  Interestingly, the intervention that produced the best results (Reducing Zero Sum Perceptions) was the least based on past psychological research.  As such, when trying to reduce the hostility in the current political environment, I advise paying attention to the nuances of the current sources of hostility.  As time goes by, the issues that divide us change.  Our attempts to bridge those gaps should adapt with them.

-Charlie Ebersole

To learn more about the interventions we used, see this document: Civil Politics Contest Study-Materials

To learn more about the study in general, see this project’s page on the Open Science Framework

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Want to Reduce Political Extremism? Ask How Instead of Why

1. What They Did – Intervention Summary:

 All participants began by rating their positions on six political policies. They then self-assessed their understanding of these policies using a series of 7-point rating scales. Next, some participants were asked to explain in detail how one of the policies works, while other participants were asked only to list the reasons they had for holding their position on that policy. Finally, all participants were asked to rerate both their understanding of the policy and their position on the policy. Participants repeated this process for one additional issue.

2. What They Found – Results:

Those who had had to explain how the political policies worked became less confident in their understanding of those policies than did those who had been asked to enumerate reasons for their positions. Further, participants reported more moderate attitudes towards the issues after giving mechanistic explanations, whereas enumerating reasons led to no such change in position extremity.

3. Who Was Studied – Sample:

MTurk users- 50% male, 50% female

4. Study Name:

Fernbach et al., 2013, Study 2

5. Citation:

Fernbach, P. M., Rogers, T., Fox, C. R., & Sloman, S. A. (2013). Political extremism is supported by an illusion of understanding. Psychological Science, XX(X), 1-8. doi:10.1177/0956797612464058

6. Link:

http://scholar.harvard.edu/files/todd_rogers/files/political_extremism.pdf

7. Intervention categories: 

generating mechanistic explanations, mTurk

8. Sample size:

112

9. Central Reported Statistic:

 “the decrement in understanding after enumerating reasons was smaller than the decrement following mechanistic explanation, as reflected by a significant interaction between judgment timing and condition, F(1, 110) = 6.64, p < .01, ηp2 = .057. With regard to extremity of positions, there was no change after enumerating reasons, F(1, 64) < 1, n.s. Moreover, as predicted, the change in position in the reasons conditions was smaller than in the mechanism conditions, as reflected by a significant interaction between judgment timing and condition on extremity scores, F(1, 110) = 3.90, p < .05, ηp2 = .034.”

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Our goal is to educate the public about social science research on improving inter-group relations across moral divides.